Using LIDAR to Validate the Performance of Vehicle Classification Stations

Vehicle classification is an important traffic parameter for transportation planning and infrastructure management. Vehicle classification stations depend on accurate calibration and validation to yield meaningful results. Operating agencies spend millions of dollars to deploy vehicle classification stations to collect classified count data, yet very few of these stations are ever subjected to a rigorous performance evaluation to ensure that they are reporting accurate data. To date such performance monitoring has been prohibitively labor intensive and prone to human error. To address this problem we develop a classification performance monitoring system to allow operating agencies to rapidly assess the health of their classification stations on a per vehicle basis. We eliminate most of the labor demands and instead, deploy a portable non-intrusive vehicle classification system (PNVCS) to classify vehicles, concurrent with an existing classification station. This paper uses a LIDAR based PNVCS but our approach is compatible with many other PNVCSs. The processing requires several intermediate steps, developed herein, including synchronizing the independent clocks and matching observations of a given vehicle between the two classification systems. The performance monitoring methodology automatically compares the vehicle classification between the existing classification station and the PNVCS for each vehicle. If the two classification systems agree, the given vehicle is automatically taken as a success. A human only looks at the vehicle when the two systems disagree, and for this task we have developed tools to semi-automate the manual validation process, greatly increasing the efficiency and accuracy of the human user. Thus, the PNVCS must be accurate enough to be used as a baseline, as verified herein. The methodology is applied to over 21,000 vehicles from several permanent and temporary vehicle classification stations to evaluate the performance of axle and length-based classification stations. The automated process does the bulk of the work, with only 8% of the vehicles requiring manual intervention. The user typically spent 3-5 sec per vehicle reviewed, translating into only a few minutes to process the exceptions from all lanes over one hour of data. This approach offers a cost effective solution to ensure that classification stations are providing accurate data, and for permanent classification stations the additional labor is a fraction of the cost to deploy the station in the first place.